IDEAS home Printed from https://ideas.repec.org/a/vrs/poicbe/v16y2022i1p1283-1294n4.html
   My bibliography  Save this article

Process Optimization Models Using Artificial Intelligence and Digital Transformation of The Insurance Industry

Author

Listed:
  • Radu Nicoleta

    (Doctoral School of Finance, University of Economic Studies, 6 Piata Romana, 1 district, Bucharest, 010374 Romania)

  • Alexandru Felicia

    (University of Economic Studies, 6 Piata Romana, 1st district, Bucharest, 010374 Romania)

Abstract

In recent years, there have been fundamental changes worldwide in the field of the business models, in the way these create value and compete, and in the field of services supplied to customers, which have been driven by the way technology is used to interact with them all. All these changes must be supported by the appropriate strategy, a scalable infrastructure and modern technology capable of delivering: target achievement, resilience, scalability, security, information privacy and technological innovation. The insurance industry pioneer’s adaptability to new technologies, considering its clear benefits in terms of improving customer service, mitigating risk and last but not least, increasing the profitability of the business itself. This study aims to analyze how the company which manages the mandatory home insurance system in Romania, Romanian Insurance Pool Against Natural Disaster – PAID S.A has been integrated in this process of digital transformation and to present the circumstances in which the solutions were developed and implemented as well as the effects generated by them.

Suggested Citation

  • Radu Nicoleta & Alexandru Felicia, 2022. "Process Optimization Models Using Artificial Intelligence and Digital Transformation of The Insurance Industry," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 16(1), pages 1283-1294, August.
  • Handle: RePEc:vrs:poicbe:v:16:y:2022:i:1:p:1283-1294:n:4
    DOI: 10.2478/picbe-2022-0117
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/picbe-2022-0117
    Download Restriction: no

    File URL: https://libkey.io/10.2478/picbe-2022-0117?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:poicbe:v:16:y:2022:i:1:p:1283-1294:n:4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.